A Transcriptional Regulatory Atlas of Coronavirus Infection of Human Cells

bioRxiv preprint doi: https://doi.org/10.1101/2020.04.24.059527; this version posted May 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 A transcriptional regulatory atlas of coronavirus infection of

2 human cells

3 Scott A Ochsner & Neil J McKenna

4 The Signaling Pathways Project and Department of Molecular and

5 Cellular Biology, Baylor College of Medicine, Houston, TX 77030

6 Address Correspondence To:

7 Neil J. McKenna

8 Department of Molecular and Cellular Biology

9 Baylor College of Medicine

10 Houston, TX 77030

11 USA

12 e: [email protected]

13 t: 713-798-8568

15 Abstract

16 Identifying transcriptional responses that are most consistently associated with

17 experimental coronavirus (CoV) infection can help illuminate human cellular signaling

18 pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from

19 publically archived CoV infection transcriptomic datasets into consensus regulatory

20 signatures, or consensomes, that rank genes based on their transcriptional

21 responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2

22 subtypes. We computed overlap between genes with elevated rankings in the CoV

23 consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly

24 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we

25 identified robust overlap between their highly ranked genes and high confidence targets

26 of signaling pathway nodes with known roles in CoV infection. We then developed a

27 series of use cases that illustrate the utility of the CoV consensomes for hypothesis

28 generation around mechanistic aspects of the cellular response to CoV infection. We

29 make the CoV infection datasets and their universe of underlying data points freely

30 accessible through the Signaling Pathways Project web knowledgebase at

31 https://www.signalingpathways.org/datasets/index.jsf.

33 Introduction

34 Infection by coronaviruses (CoV) represents a major current global public health

35 concern. Signaling within and between airway epithelial and immune cells in response

36 to infections by CoV and other viruses is coordinated by a complex network of signaling

37 pathway nodes. These include chemokine and cytokine-activated receptors, signaling

38 enzymes and transcription factors, and the genomic targets encoding their downstream

39 effectors (Takeda et al., 2003; Stark et al., 1998; Darnell et al., 1994)]. We recently

40 described the Signaling Pathways Project (SPP; (Ochsner et al., 2019), an integrated

41 ‘omics knowledgebase designed to assist bench researchers in leveraging publically

42 archived transcriptomic and ChIP-Seq datasets to generate research hypotheses. A

43 unique aspect of SPP is its collection of consensus regulatory signatures, or

44 consensomes, which rank genes based on the frequency of their significant differential

45 expression across transcriptomic experiments mapped to a specific signaling pathway

46 node or node family. By surveying across multiple independent datasets, we have

47 shown that consensomes recapitulate pathway node-genomic target regulatory

48 relationships to a high confidence level (Ochsner et al., 2019).

49 Placing the transcriptional events associated with human CoV infection in context with

50 those associated with other signaling paradigms has the potential to catalyze the

51 development of novel therapeutic approaches. The CoV research community has been

52 active in generating and archiving transcriptomic datasets documenting the

53 transcriptional response of human cells to infection by the three major CoV species,

54 namely, Middle East respiratory syndrome coronavirus (MERS) and severe acute

55 respiratory syndrome coronaviruses 1 (SARS1) and 2 (SARS2) (DeDiego et al., 2011;

56 Josset et al., 2013; Sims et al., 2013; Yoshikawa et al., 2010). To date however the field

57 has lacked a resource that fully capitalizes on these datasets by, firstly, using them to

58 rank human genes according to their transcriptional response to CoV infection and

59 secondly, contextualizing these transcriptional responses by integrating them with

60 ‘omics data points relevant to host cellular signaling pathways. Here, as a service to the

61 research community to catalyze the development of novel CoV therapeutics, we

62 generated consensomes for infection of human cells by MERS, SARS1 and SARS2

63 CoVs. We then analyzed the extent to which high confidence transcriptional targets for

64 these viruses intersected with genes with elevated rankings in transcriptomic and ChIP-

65 Seq consensomes for cellular signaling pathway nodes. Integration of the CoV

66 consensomes with the existing universes of SPP transcriptomic and ChIP-Seq data

67 points in a series of use cases illuminates previously uncharacterized intersections

68 between CoV infection and human cellular signaling pathways. The CoV infection

69 consensome and its underlying datasets provide researchers with a unique and freely

70 accessible framework within which to generate and pressure test hypotheses around

71 human cellular signaling pathways impacted by CoV infection.

75 Results

76 Generation of the CoV consensomes

77 We first set out to generate a set of consensomes ranking human genes based on the

78 frequency of their significant differential expression in response to infection by MERS,

79 SARS1 and SARS2 CoVs. To do this we searched the Gene Expression Omnibus

80 (GEO) and ArrayExpress databases to identify datasets involving infection of human

81 cells by these species. From this initial collection of datasets, we next carried out a

82 three step quality control check as previously described (Ochsner et al., 2019), yielding

83 a total of 3,041,047 million data points in 111 experiments from 25 independent CoV

84 infection transcriptomic datasets (Supplementary information, Section 1). Using these

85 curated datasets, we next generated consensomes for each CoV species, as well as

86 one ranking genes across all CoV infection experiments (ALL CoV). As a reference

87 consensome for a virus whose transcriptional impact on human cells has been studied

88 in depth, we also generated a consensome for human influenza A virus (IAV) infection.

89 The Supplementary information files contain the full human ALL CoV (Section 2), MERS

90 (Section 3), SARS1 (Section 4), SARS2 (Section 5) and IAV (Section 6) infection

91 transcriptomic consensomes. To assist researchers in inferring transcriptional regulation

92 of signaling networks, the ALL CoV consensome is annotated to indicate the identity of

93 a gene as encoding a bioactive small molecule, receptor, signaling enzyme or

94 transcription factor (Supplementary information, Section 2, columns W-Z). As an initial

95 benchmark for the ALL CoV consensome, we assembled a list of 20 interferon-

96 stimulated genes (ISGs), which encode many of the key canonical viral response

97 factors (Supplementary information, Section 2, column I) (Schneider et al., 2014). As

98 shown in the scatterplot representation of the ALL COV consensome in Figure 1, all

99 canonical ISGs were assigned appropriately elevated rankings in the consensome.

100 To gain insight into transcriptional intersections between CoV infection and human

101 cellular signaling pathways, we next computed genes with elevated rankings in the CoV

102 consensomes against genes with high confidence regulatory relationships with cellular

103 signaling pathway nodes. To do this we generated five lists of genes corresponding to

104 the ALL CoV, MERS, SARS1, SARS2 and IAV transcriptomic consensome 95th

105 percentiles. We then retrieved genes in the 95th percentiles of human transcriptomic (n

106 = 30) consensomes and ChIP-Seq (n = 834) consensomes for a collection of cellular

107 signaling pathway nodes, and computed the extent and significance of their overlap with

108 genes in the 95th percentiles of each of the five CoV consensomes. Significant overlap

109 between the 95th percentiles of a node/node family consensome and a CoV

110 consensome would indicate a potential biological relationship between loss or gain of

111 function of that node and the transcriptional response to CoV infection.

112 For clarity and brevity we will refer from here on to the 95th percentile of a ChIP-Seq

113 consensome as “CC95” and the 95th percentile of a transcriptomic consensome as

114 “TC95”. Of the 834 node CC95s evaluated, 377 had significant overlap (p < 0.05) with at

115 least one of the CoV infection TC95s; of the 30 node/node family TC95s evaluated, 25

116 had significant overlap (p< 0.05) with at least one of the CoV infection TC95s. Results of

117 these analyses are shown in Figure 2 (receptor and enzyme transcriptomic analysis),

118 Figure 3 (ChIP-Seq transcription factor analysis), Figure 4 (ChIP-Seq enzyme analysis)

119 and Figure 5 (ChIP-Seq co-node analysis); see also Supplementary information Section

120 7 for the complete numerical data. We next surveyed the significant overlaps to identify

121 (i) canonical inflammatory signaling pathway nodes with characterized roles in the

122 response to CoV infection, thereby validating the consensome approach in this context;

123 and (ii) evidence for previously uncharacterized transcriptional biology of CoV infection

124 that are consistent with their roles in the response to other viral infections.

125 Receptors Reflecting their well-documented roles in the response to viral infection, we

126 observed appreciable significant overlap between all TC95s and those for the toll-like

127 (TLR (Totura et al., 2015), interferon (IFNR (Hensley et al., 2004)) and tumor necrosis

128 factor (TNFR (W. Wang et al., 2007)) receptor families (Fig. 2). Interestingly, these

129 signatures were particularly highly enriched in the SARS2 TC95 – potentially reflecting a

130 particularly strong antiviral response to infection by SARS2. TC95 overlaps for receptor

131 systems with previously uncharacterized connections to CoV infection, including

132 epidermal growth factor receptors, glucocorticoid receptor and NOTCH receptor

133 signaling, are consistent with the known roles of these receptor systems in the context

134 of other viral infections (Hayward, 2004; Ito et al., 2011; Ng et al., 2013; Ostler et al.,

135 2019; Zheng et al., 2014). The relatively strong enrichment for xenobiotic receptors

136 reflects work describing a role for pregnane X receptor in innate immunity (S. Wang et

137 al., 2014) and points to a potential role for members of this family in the response to

138 CoV infection.

139 Transcription factors In general, ChIP-Seq enrichments for transcription factors and

140 other nodes were more specific for individual CoV infection TC95s (compare Fig. 2 with

141 Figs 3, 4 and 5). This is likely due to the fact that ChIP-Seq consensomes are based on

142 direct promoter binding by a specific node antigen, whereas transcriptomic

143 consensomes encompass both direct and indirect targets of specific receptor and

144 enzyme node families. Not unexpectedly – and speaking again to validation of the

145 consensomes - the strongest and most significant CoV TC95 overlaps were observed

146 for CC95s for known transcription factor mediators of the transcriptional response to

147 CoV infection, including members of the NFκB (Ludwig & Planz, 2008; Poppe et al.,

148 2017; Ruckle et al., 2012), IRF (Chiang & Liu, 2018) and STAT (Blaszczyk et al., 2016;

149 Frieman et al., 2007; Garcia-Sastre et al., 1998) transcription factor families. Consistent

150 with its known role in the regulation of interferon-stimulated genes (Hasan et al., 2013),

151 we also observed appreciable overlap between the CC95 for TFEB and the ALL CoV

152 TC95. Moreover, the strong overlap between the GTF2B/TFIIB CC95 and all viral

153 TC95s reflects previous evidence identifying GTF2B as a target for orthomyxovirus

154 (Haas et al., 2018), herpes simplex virus (Gelev et al., 2014) and hepatitis B virus

155 (Haviv et al., 1998).

156 Enzymes Compared to the roles of receptors and transcription factors in the response

157 to viral infection, the roles of signaling enzymes are less well illuminated – indeed, in the

158 context of CoV infection, they are entirely unstudied. Through their regulation of cell

159 cycle transitions, cyclin-dependent kinases play important roles in the orchestration of

160 DNA replication and cell division, processes that are critical in the viral life cycle. CDK6,

161 which has been suggested to be a critical G1 phase kinase (Bellail et al., 2014; Grossel

162 & Hinds, 2006), has been shown to be targeted by a number of viral infections, including

163 Kaposi's sarcoma-associated herpesvirus (Kaldis et al., 2001) and HIV-1 (Pauls et al.,

164 2014). Consistent with this common role across distinct viral infections, we observed

165 robust overlap between the CDK95 TC95 (Fig. 2) and the CDK6 CC95 (Fig. 4) and

166 those of all viral TC95s. As with the TLRs, IFNRs and TNFRs, which are known to

167 signal through CDK6 (Cingoz & Goff, 2018; Handschick et al., 2014; Hennessy et al.,

168 2011), overlap with the CDK6 CC95 was particularly strong in the case of the SARS2

169 TC95 (Fig. 4). CCNT2 is a member of the highly conserved family cyclin family and,

170 along with CDK9, is a member of the viral-targeted p-TEFB complex (Zaborowska et al.,

171 2016). Reflecting a potential general role in viral infection, appreciable overlap was

172 observed between the CCNT2 CC95 and all viral TC95s (Fig. 4). Finally in the context

173 of enzymes, DNA Topoisomerase (TOP1) has been shown to be required for efficient

174 replication of simian virus 40 (Wobbe et al., 1987) and Ebola (Takahashi et al., 2013)

175 viruses. The prominent overlap between its CC95 and those of SARS2 and IAV (Fig. 4)

176 suggest that it may play a similar role in facilitating the replication of these viruses.

177 Co-nodes We have coined the term “co-nodes” as a convenient catch-all for cellular

178 factors that are not members of the three principal signaling pathway node categories

179 (receptors, enzymes and transcription factors; Ochsner et al., 2019). The breadth of

180 functions encompassed by members of this category reflects the diverse mechanisms

181 employed both by viruses to infect and propagate in cells, as well as by hosts to mount

182 an efficient immune response. Consistent with its characterized role in the recruitment

183 of p-TEFB by IV-1 Tat protein (Schulze-Gahmen et al., 2013), we observed consistently

184 strong enrichment of the AFF4 CC95 in all viral TC95s (Fig. 5). The targeting of CNOT3

185 for degradation in response to adenoviral infection (Chalabi Hagkarim et al., 2018) is

186 reflected in the significant overlap between its CC95 and the viral TC95s, particularly in

187 the case of the SARS2 (Fig. 5).

188 By way of additional independent validation of the CoV and IAV consensomes, Gene

189 Set Enrichment Analysis (Subramanian et al., 2005) reflected significant overlap

190 between the CoV and IAV TC95s and a variety of viral infection and inflammatory

191 transcription factor target gene sets (Supplementary information Section 8).

192

193 Use cases

194 Having established the reliability of the consensome approach in the context of CoV

195 infection, we next wished to use the CoV consensomes to gather evidence to identify

196 previously uncharacterized mediators of the transcriptional response to infection by

197 CoVs and, of particular current interest, SARS2.

198

199 Use case 1: consensome redundancy analysis identifies potential

200 uncharacterized players in the response to CoV infection

201 We previously showed (Figs. 2 and 3) that the overlap of the CoV TC95 genes was

202 most robust among consensomes for members of the IFNR, TLR and TNF receptor

203 families (Fig. 2), and the NFkB, RELA, IRF and STAT transcription factor families (fig.

204 5). To investigate this further, we ranked genes in the ALL CoV consensome by their

205 aggregate 80th percentile rankings across these consensomes (Supplementary

206 information, Section 2, column V). Transcriptomic consensomes for IFNRs (Section 9),

207 TLRs (Section 10) and TNFRs (Section 11) are provided in Supplementary information,

208 as are links to the ChIP-Atlas lists used to generate the ChIP-Seq consensomes

209 (Section 12).

210 This redundancy ranking elevates genes with known critical roles in the response to

211 viral infection that are acutely responsive to a spectrum of inflammatory signaling

212 nodes, such as NCOA7 (Doyle et al., 2018), STAT (Chapgier et al., 2009) and TAP1

213 (Gruter et al., 1998). Interestingly, genes such as PSMB9, CSRNP1 and MRPL24 have

214 ALL CoV discovery rates that are comparable to or exceed those of many of the classic

215 viral response ISGs (Fig. 1), but are either largely or completely uncharacterized in the

216 context of viral infection. This use case the reflects the potential of consensome-driven

217 data mining to illuminate novel and potentially therapeutically relevant transcription

218 pathway effectors in the response to CoV infection.

219

220 Use case 2: discrimination between genes frequently differentially expressed in

221 response to CoV, but not IAV, infection

222 We next wished to gain insight into signaling paradigms that were selectively

223 characteristic of CoV infection but not IAV infection. To do this we generated a set of

224 genes that were frequently differentially expressed in response to CoV infection (ALL

225 CoV TC99), but less frequently expressed in response to IAV infection (50th percentile)

226 (Table 1). Interestingly, this group contained two genes encoding transcription factors

227 with well characterized roles in the regulation of oscillatory gene expression, PER1 and

228 PER2 (Yamajuku et al., 2010). This led us to speculate that genomic targets of

229 transcriptional regulators of circadian rhythms might be enriched among high

230 confidence CoV-regulated genes, but not IAV-regulated genes. Consistent with this we

231 observed significant overlap between the CC95s of the master circadian transcription

232 factors ARNTL1/BMAL1 and CLOCK (Gaucher et al., 2018) and the CoV TC95, but not

233 the IAV TC95 (Fig. 2). These data indicate a hitherto uncharacterized intersection

234 between CoV signaling and the cellular oscillatory apparatus. Another gene of note in

235 this group is INHBA, encoding activin A, overexpression of which in murine lung gives

236 rise to a phenotype resembling acute respiratory distress-like syndrome (Apostolou et

237 al., 2012), a condition commonly associated with CoV infection (Totura & Bavari, 2019).

238 Other genes in the group provide evidence for the potential mechanisms of infection

239 and propagation of CoVs. TJAP1, for example, encodes a member of a family of

240 proteins involved in regulation of tight junctions, shown to be route of porcine epidemic

241 diarrhea coronavirus entry into epithelial cells (Luo et al., 2017). In addition, GON7 is a

242 member of the KEOPS complex (Wan et al., 2017), which is involved in

243 threonylcarbamoylation of tRNAs, which represent an important host facet of the

244 retroviral life cycle (Jin & Musier-Forsyth, 2019). Interestingly, another critical gene in

245 this tRNA pathway, YRDC, is the 19th ranked gene in the ALL CoV consensome

246 (Supplementary information Section 2).

247

248 Use case 3: evidence for antagonism between progesterone receptor and

249 interferon receptor signaling in the airway epithelium

250 Although a lack of clinical data has so far prevented a definitive evaluation of the

251 connection between pregnancy and susceptibility to SARS2 infection in COVID-19,

252 pregnancy has been previously associated with the incidence of viral infectious

253 diseases, particularly respiratory infections (Sappenfield et al., 2013; Siston et al.,

254 2010). We were interested therefore to see consistent overlap between the

255 progesterone receptor (PGR) TC95 and all the viral TC95s, with the enrichment being

256 particularly evident in the case of the SARS2 TC95 (Fig. 2). To investigate the specific

257 nature of the crosstalk implied by this overlap in the context of the airway epithelium, we

258 first identified a set of 16 genes that were in both the ALL CoV and PGR TC95s. We

259 then retrieved two SPP experiments involving treatment of A549 airway epithelial cells

260 with the PGR full antagonist RU486 (RU), alone or in combination with the GR agonist

261 dexamethasone (DEX). As shown in Figure 6 , there was nearly unanimous correlation

262 in the direction of regulation of all 16 genes in response to CoV infection and PGR loss

263 of function. These data indicate that antagonism between PGR and IFNR signaling in

264 the airway epithelium may predispose pregnant women to infection by SARS2.

265

266 Use case 4: evidence for a role for the telomerase catalytic subunit TERT in the

267 interferon response to viral infection

268 Although telomerase activation has been well characterized in the context of cell

269 immortalization by human tumor virus infection (Gewin et al., 2004; Klingelhutz et al.,

270 1996; Yang et al., 2004), no connection has previously been made between CoV or IAV

271 infection and telomerase. We were therefore intrigued to observe robust overlap

272 between all viral TC95s and that of the telomerase catalytic subunit TERT. In support of

273 this finding, NFkB signaling has been shown to induce expression (Yin et al., 2000) and

274 nuclear translocation (Akiyama et al., 2003) of TERT, and direct co-regulation by

275 telomerase of NFkB-dependent transcription has been linked to chronic inflammation

276 (Ghosh et al., 2012). Inspecting the ALL CoV consensome underlying data points (data

277 not shown) we found that the TERT gene was not transcriptionally induced in response

278 to infection by any of the CoVs, indicating that the overlap between its TC95 and those

279 of the CoVs might occur in response to an upstream regulatory signal. If functional

280 interactions between TERT and inflammatory nodes did indeed take place in response

281 to CoV infection, we anticipated that this would be reflected in close agreement

282 regarding the direction of differential expression of CoV infection-regulated genes in

283 response to perturbation of TERT on the one hand, and on the other, to stimulation of

284 the classic viral response IFNRs. To test this hypothesis, we took the same set of 20

285 ISGs referred to previously (Fig. 1) and compared their direction of regulation across all

286 experiments underlying the ALL CoV, TERT and IFNR consensomes. For reference, the

287 TERT consensome (Section 13) and its underlying data points (Section 14) are

288 provided in the Supplementary information. With respect to the IFNR and TERT data

289 points, we observed a nearly universal alignment in the direction of regulation of all

290 genes tested with those in the CoV infection experiments (Fig. 6), with agreement in the

291 direction of regulation across 99% of the underlying probesets. We should note that of

292 the 1859 p < 0.05 CoV infection ISG data points, we observed repression, rather than

293 induction, in response to CoV infection in 303 (~15%) , which may be attributable to the

294 impact of differences in cell type, cell cycle stage or virus incubation time across the

295 independent experiments. Our results are consistent with a model in which activation of

296 telomerase is a component of the human innate immune response to viral infection.

297

298 Use case 5: SARS2 infection of human cells is specifically associated with an

299 epithelial to mesenchymal transition transcriptional signature

300 Epithelial to mesenchymal transition (EMT) is the process by which epithelial cells lose

301 their polarity and adhesive properties and acquire the migratory and invasive 14

302 characteristics of mesenchymal stem cells (Lamouille et al., 2014). EMT is known to

303 contribute to pulmonary fibrosis (Hill et al., 2019) and acute interstitial pneumonia (H. Li

304 et al., 2014), both of which have been reported in connection with SARS2 infection in

305 COVID-19 (Adair & Ledermann, 2020; P. Zhou et al., 2020). Moreover, EMT is widely

306 accepted as a core component of the process by which renal tubular cells transform into

307 mesenchymal cells during the development of fibrosis in kidney disease (Y. Liu, 2006),

308 a signature comorbidity of SARS2 infection (Durvasula et al., 2020). Of the 834 CC95s

309 analyzed, overlap (p < 0.05) was specific to the SARS2 CC95 for only five; SNAI2/Slug,

310 SOX2, GATA6, CTBP1 and PRMT1. Strikingly, a literature search indicated that these

311 five nodes were connected by documented roles in in the promotion of EMT (Avasarala

312 et al., 2015; Herreros-Villanueva et al., 2013; Martinelli et al., 2017; Nieto, 2002; Sahu

313 et al., 2017). In addition to these nodes, whose C95 genes were exclusively enriched (p

314 < 0.05) in the SARS2 C95 genes, we identified several other EMT-linked nodes whose

315 CC95 genes were preferentially enriched (p < 0.05) in the SARS2 TC95 genes (Figs. 3-

316 5), including the homeodomain transcription factor SIX2 (C.-A. Wang et al., 2014),

317 SMAD4 (Siraj et al., 2019), and the co-nodes PYGO2 (Chi et al., 2019), SKI (Tecalco-

318 Cruz et al., 2018), BRD7 (T. Liu et al., 2017) and STAG2 (Nie et al., 2019).

319 To investigate this further, we computed overlap between the individual viral TC99s and

320 a list of 335 genes manually curated from the research literature as signature EMT

321 markers (Supplementary information Section 15; Zhao et al., 2015). Consistent with the

322 node consensome enrichment analysis, we observed significant enrichment of

323 members of this gene set within the SARS2 CC99, but not those of the ALL CoV,

324 SARS1, MERS or IAV consensomes (Supplementary information Section 16). One

325 possible explanation for this was the fact that the SARS2 consensome was comprised

326 of airway epithelial cell lines, whereas the SARS1 and MERS consensomes included

327 non-epithelial cell biosamples (Supplementary information Section 1). To exclude this

328 possibility therefore, we next calculated airway epithelial cell-specific consensomes for

329 SARS1 and MERS and computed overlap of their TC95s against the 864 pathway

330 node/node family CC95s & TC95s. We found that significant overlap with the CoV

331 TC95s remained specific to SARS2 (data not shown), confirming that significant overlap

332 with the EMT node signature was specific to the SARS2 TC99.

333 We next applied consensome redundancy analysis (see Use case 1) to isolate a set of

334 SARS2 regulated genes (CPV P < 0.05) that were high confidence targets (i.e. in the

335 CC80) for at least two of the EMT nodes (SNAI2, SOX2, GATA6, CTBP1 and PRMT1;

336 Supplementary information, Section 4, column M). A literature survey showed that 13 of

337 these 21 genes had a documented connection to EMT (Supplementary information,

338 Section 5, column N). Figure 9 compares the percentile rankings for these genes across

339 the three CoV infection consensomes and the IAV consensome. Although some EMT

340 genes, such as CXCL2 and IRF9, had elevated rankings across all four consensomes,

341 the collective EMT gene signature had a significantly higher mean percentile value in

342 the SARS2 consensome than in each of the three others (Fig. 9).

343 Although EMT has been associated with infection by transmissible gastroenteritis virus

344 (Xia et al., 2017), this is to our knowledge the first evidence connecting CoV infection,

345 and specifically SARS2 infection, to EMT. Interestingly, several members of the group

346 of SARS2-induced EMT genes have been associated with signature pulmonary

347 comorbidities of CoV infection, including ADAR (Diaz-Pina et al., 2018), CLDN1

348 (Vukmirovic et al., 2017) and SOD2 (Gao et al., 2008). Of note in the context of these

349 data is the fact that signaling through two SARS2 cellular receptors, ACE2/AT2 and

350 CD147/basigin, has been linked to EMT in the context of organ fibrosis (Kato et al.,

351 2011; Ruster & Wolf, 2011; C. Wang et al., 2018). Moreover, overlap between of the

352 CoV TC95s and the TERT CC95 was particularly robust in the case of SARS2, a finding

353 of potential relevance to the fact that telomerase has been implicated in EMT (Z. Liu et

354 al., 2013). Collectively, our data indicate that EMT warrants further investigation as a

355 SARS2-specific pathological mechanism.

356 Development of a CoV infection cell signaling knowledgebase

357 Having validated the ALL CoV consensome, we next wished to make it freely available

358 to the research community for re-use in the characterization of signaling events

359 associated with CoV infection. Firstly, the viral infection datasets were curated

360 accordingly to our previously described protocol (Ochsner et al., 2019) made available

361 for browsing in the SPP Dataset listing

362 (https://www.signalingpathways.org/datasets/index.jsf). As with other SPP datasets, and

363 per FAIR data best practice, CoV infection datasets were associated with detailed

364 descriptions, assigned a digital object identifier, and linked to the associated article to

365 place the dataset in its original experimental context. Loading the CoV datasets into the

366 SPP also automatically made the underlying data points discoverable by the SPP query

367 tool Ominer (Ochsner et al., 2019). These reports represent a summary of the current

368 state of transcriptomic and ChIP-Seq knowledge on the regulatory relationship of a

369 given gene with upstream regulatory pathway nodes, or in clinical and model

370 experiments. The full value of the integration of the CoV consensome with the existing

371 SPP framework can perhaps be best appreciated in the context of the one click links to

372 Ominer Regulation Reports from the individual CoV datasets. These Reports provide

373 researchers with a wealth of contextual data on signaling pathways impacted by CoV

374 infection in the context of a specific gene. Table 2 shows links to Regulation Reports for

375 the top twenty ranked genes in the ALL CoV consensome. The order of sections in the

376 Reports is Receptors, Enzymes, Transcription Factors, Co-nodes, Clinical and Models,

377 the last section including data points from the CoV infection model experiments that

378 form the basis of this study.

379 Discussion

380 An effective research community response to the impact of CoVs on human health

381 demands systematic exploration of the transcriptional interface between viral infection

382 and human cell signaling systems. It also demands routine access to existing datasets

383 that is unhindered either by paywalls or by lack of the informatics training required to

384 manipulate archived datasets in their unprocessed state. Moreover, the substantial

385 logistical obstacles to BSL3 certification only emphasizes the need for fullest possible

386 access to, and re-usability of, existing CoV infection datasets to focus and refine

387 hypotheses prior to carrying out in vivo CoV infection experiments. To this end, we

388 generated a set of CoV infection consensomes that rank human genes by the

389 reproducibility of their significant differential expression in response to infection of

390 human cells by CoVs. We then computed the CoV consensomes against high

391 confidence transcriptional signatures for a broad range of cellular signaling pathway

392 nodes, affording investigators with a broad range of signaling interests an entrez into

393 the study of CoV infection of human cells. The five use cases described here represent

394 illustrative examples of the types of analyses that users are empowered to carry out in

395 the CoV infection knowledgebase.

396 To democratize access to the CoV consensome and its >3,000,000 underlying data

397 points by the broadest possible audience, we have integrated them into the SPP

398 database to create a cell signaling knowledgebase for CoV infection. Incorporation of

399 the CoV data points into SPP in this manner places them in the context of millions more

400 existing SPP data points documenting transcriptional regulatory relationships between

401 pathway nodes and their genomic targets. In doing so, we afford users a unique

402 appreciation of the cellular signaling pathway nodes whose gain or loss of function in

403 response to CoV infection gives rise to these transcriptional patterns.

404 The human CoV and IAV consensomes and their underlying datasets are “living”

405 resources on SPP that will be updated and versioned with appropriate datasets. This

406 will be particularly important in the case of SARS2, as datasets involving infection of

407 human cells with this virus are necessarily currently limited in number. This will allow

408 for hardening of observations that are intriguing, but whose significance is currently

409 unclear, such as the overlap of the CoV TC95s with the TERT TC95, as well as the

410 enrichment of EMT genes among those with elevated rankings in the SARS2

411 consensome. We welcome feedback and suggestions from the research community for

412 the future development of the SPP CoV infection consensomes.

413

414

415

416

417

418 Methods

419 Dataset processing and consensome analysis.

420 Differential expression values were calculated for each gene in each experiment using

421 the limma analysis package from Bioconductor then committed to the consensome

422 analysis pipeline as previously described (Ochsner et al., 2019). Briefly, the

423 consensome algorithm surveys each experiment across all datasets and ranks genes

424 according to the frequency with which they are significantly differentially expressed. For

425 each transcript, we counted the number of experiments where the significance for

426 differential expression was ≤0.05, and then generated the binomial probability, referred

427 to as the consensome p-value (CPV), of observing that many or more nominally

428 significant experiments out of the number of experiments in which the transcript was

429 assayed, given a true probability of 0.05. A more detailed description of the

430 transcriptomic consensome algorithm is available (Ochsner et al., 2019). The

431 consensomes and underlying datasets were loaded into an Oracle 13c database and

432 made available on the SPP user interface as previously described (Ochsner et al.,

433 2019).

434 Statistical analysis

435 Gene Overlap analysis was performed using the Bioconductor GeneOverlap analysis

436 package

437 (https://www.rdocumentation.org/packages/GeneOverlap/versions/1.8.0/topics/GeneOv

438 erlap) implemented in R. Briefly, given a whole set I of IDs and two sets A ∈ I and B ∈ I,

439 and S = A ∩ B, GeneOverlap calculates the significance of obtaining S. The problem is

440 formulated as a hypergeometric distribution or a contingency table, which is solved by

441 Fisher’s exact test. The universe for the overlap was set at a recent estimate of the total

442 number of coding genes in the human genome (21,500; Pertea et al., 2018). Paired

443 Two Sample t-Test for comparing the mean percentile ranking of EMT genes in the

444 MERS, SARS1, SARS2 and IAV consensomes was performed in PRISM at 12 degrees

445 of freedom.

446 Consensome generation

447 The procedure for generating transcriptomic consensomes has been previously

448 described (Ochsner et al., 2019). To generate the ChIP-Seq consensomes, we first

449 retrieved processed gene lists from ChIP-Atlas, which rank human genes based upon

450 their average MACS2 occupancy across all publically archived datasets in which a

451 given transcription factor, enzyme or co-node is the IP antigen. Of the three stringency

452 levels available (10, 5 and 1 kb from the transcription start site), we selected the most

453 stringent (1 kb). According to SPP convention (Ochsner et al., 2019), we then mapped

454 the IP antigen to its pathway node category and class, and the experimental cell line to

455 its appropriate biosample physiological system and organ. We then organized the

456 ranked lists into percentiles to generate the node ChIP-Seq consensome.

457 SPP web application

458 The SPP knowledgebase is a gene-centric Java Enterprise Edition 6, web-based

459 application around which other gene, mRNA, protein and BSM data from external

460 databases such as NCBI are collected. After undergoing semiautomated processing

461 and biocuration as described above, the data and annotations are stored in SPP’s

462 Oracle 13c database. RESTful web services exposing SPP data, which are served to

463 responsively designed views in the user interface, were created using a Flat UI Toolkit

464 with a combination of JavaScript, D3.JS, AJAX, HTML5, and CSS3. JavaServer Faces

465 and PrimeFaces are the primary technologies behind the user interface. SPP has been

466 optimized for Firefox 24+, Chrome 30+, Safari 5.1.9+, and Internet Explorer 9+, with

467 validations performed in BrowserStack and load testing in LoadUIWeb. XML describing

468 each dataset and experiment is generated and submitted to CrossRef to mint DOIs

469 (Ochsner et al., 2019).

470

471 Data availability

472 The entire set of data points used to generate the CoV consensome has been uploaded in an R

473 file to Figshare and a link included for reviewer access. The entire set of metadata for these

474 data points is available in Supplementary information Section 1. Consensome data points are in

475 Supplementary information Sections 2-6.

476 SPP is freely accessible at https://www.signalingpathways.org. Programmatic access to all

477 underlying data points and their associated metadata are supported by a RESTful API at

478 https://www.signalingpathways.org/docs/. All SPP datasets are biocurated versions of publically

479 archived datasets, are formatted according to the recommendations of the FORCE11 Joint

480 Declaration on Data Citation Principles 74, and are made available under a Creative Commons

481 CC 3.0 BY license. The original datasets are available are linked to from the corresponding SPP

482 datasets using the original repository accession identifiers. These identifiers are for

483 transcriptomic datasets, the Gene Expression Omnibus (GEO) Series (GSE); and for

484 cistromic/ChIP-Seq datasets, the NCBI Sequence Read Archive (SRA) study identifier (SRP).

485 DOIs for the consensomes and datasets are pending.

486 Code availability

487 The full SPP source code is available in the SPP GitHub account under a Creative Commons

488 CC 3.0 BY license at https://github.com/signaling-pathways-project/ominer/.

489 Acknowledgements

490 This work was supported by the National Institute of Diabetes, Digestive and Kidney

491 Diseases NIDDK Information Network U24 (DK097748), the National Cancer Institute

492 (CA125123) and by the Brockman Medical Research Foundation. We appreciate the

493 technical assistance of Apollo McOwiti and Shijing Qu of the Biostatistics and

494 Informatics Shared Resource of the Duncan NCI Comprehensive Cancer Center. We

495 thank all investigators who archive their datasets, without whom SPP would not be

496 possible.

497

498 Author contributions

499 Dataset biocuration: SO

500 Data analysis: SO, NM

501 Manuscript drafting: NM

502

503 Competing interests

504 The authors declare no competing interests.

505

506

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947

948

949 Figure Titles Legends

950 Figure 1. Ranking of ISGs in the human ALL CoV infection transcriptomic

951 consensome. See Supplementary information, Section 2, column I.

952 Figure 2. GeneOverlap analysis of CoV and IAV TC95s and SPP receptor or

953 enzyme TC95s. OR: Odds ratio. All ORs are p < 0.05. Numerical data are provided in

954 Supplementary information Section 8.

955 Figure 3. GeneOverlap analysis of CoV and IAV TC95s and ChIP-Atlas

956 transcription factor CC95s. OR: Odds ratio. All ORs are p < 0.05. Numerical data are

957 provided in Supplementary information Section 8.

958 Figure 4. GeneOverlap analysis of CoV and IAV TC95s and ChIP-Atlas enzyme

959 CC95s. OR: Odds ratio. Numerical data are provided in Supplementary information

960 Section 8. All ORs are p < 0.05.

961 Figure 5. GeneOverlap analysis of CoV and IAV TC95s and ChIP-Atlas co-node

962 CC95s. OR: Odds ratio. Numerical data are provided in Supplementary information

963 Section 8. All ORs are p < 0.05.

964 Figure 6. Antagonism between PGR and IFNR signaling in the regulation of viral

965 response genes in the airway epithelium. PGR loss of function experiments were

966 retrieved from GSE17307 (SPP DOI 10.1621/ xigKzGn1se).

967 Figure 7. Conservation of polarity of differential expression in CoV, IFNR and

968 TERT perturbation transcriptomic experiments across diverse canonical

969 interferon-inducible viral response genes. Positive/negative regulatory relationship

970 of TERT with a transcriptional target was inferred from the design of the underlying

971 experiments: this was loss of function for experiments in GSE77014 (MST132 inhibitor)

972 and GSE60175 (shRNA), and gain of function in E-MEXP-563 (overexpression). All

973 IFNR experiments were gain of function using characterized physiological ligands for

974 members of the family.

975 Figure 8. Identification of a SARS2 epithelial to mesenchymal transcriptional

976 signature. Note that the SARS2 genes are all in the 99th percentile and are therefore

977 superimposed in the scatterplot. Indicated are the results of the Paired Two Sample for

978 Means t-Test comparing the Relative Ranks of the genes in the SARS2 consensome

979 with those in the SARS1, MERS and IAV consensomes.

980

981 Tables

982 Table 1. Genes in the 99th %ile of the ALL CoV consensome and the 50th

983 percentile of the IAV consensome

984

985

986

987 Table 2. Links to SPP Regulation Reports for the top 20 ranked genes in the ALL

988 CoV consensome

Symbol Name Link JUN Jun proto-oncogene, AP-1 transcription factor subunit SPP Regulation Report DUSP1 dual specificity phosphatase 1 SPP Regulation Report FOS Fos proto-oncogene, AP-1 transcription factor subunit SPP Regulation Report EGR1 early growth response 1 SPP Regulation Report CXCL2 C-X-C motif chemokine ligand 2 SPP Regulation Report TNFAIP3 TNF alpha induced protein 3 SPP Regulation Report IER2 immediate early response 2 SPP Regulation Report CCNL1 cyclin L1 SPP Regulation Report PIM3 Pim-3 proto-oncogene, serine/threonine kinase SPP Regulation Report CSRNP1 cysteine and serine rich nuclear protein 1 SPP Regulation Report FOSB FosB proto-oncogene, AP-1 transcription factor subunit SPP Regulation Report ZC3H12A zinc finger CCCH-type containing 12A SPP Regulation Report ZFP36 ZFP36 ring finger protein SPP Regulation Report BHLHE40 basic helix-loop-helix family member e40 SPP Regulation Report RELB RELB proto-oncogene, NF-kB subunit SPP Regulation Report EGR2 early growth response 2 SPP Regulation Report NFKBIA NFKB inhibitor alpha SPP Regulation Report BCL3 BCL3 transcription coactivator SPP Regulation Report YRDC yrdC N6-threonylcarbamoyltransferase domain containing SPP Regulation Report IFIT1 interferon induced protein with tetratricopeptide repeats 1 SPP Regulation Report 989

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